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A Robust Rating Aggregation Method based on Rater Group Trustworthiness for Collusive Disturbance

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Abstract

Rating can be obligatory for many tasks, such as film recommendation, hotel rating, and product evaluation. Aggregating ratings given by numerous raters is a necessary and effective way to obtain comprehensive evaluation of the objects. While the awareness of potential distortion for some of the targeted objects, has attracted substantial attention of researchers and motivated the designing of the robust rating aggregation method to overcome the impact of disturbance from ignorant/malicious raters in practice. In this paper, we focus on rating aggregation with collusive disturbance, which is hard to be eliminated and invalidate traditional rating aggregation methods. Therefore, we will introduce the idea of detecting collusive group into rating aggregation to develop a new method, called robust rating aggregation method based on rater group trustworthiness (RGT), which obtains four main modules: Graph Mapping, Rater Group Detection, Group Trustworthiness Calculating, and Rating Aggregation. Experimental results and analyses demonstrate that our method is more robust to collusive disturbance than other traditional methods.

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Data Availability

The real datasets supporting the findings of this study are available at: http://netflixprize.com and http://grouplens.org. The source code for generating synthetic dataset in this paper is available at: http://www.wujunpla.net/work1-660.html.

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Funding

This work is supported by the Natural Science Foundation of Guangdong Province, China under Grant No. 2022A1515010661, and the Natural Science Foundation of China under Grant Nos. 72201035, 71871217 and 71731002.

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Authors and Affiliations

  1. Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China

    Huan Zhu, Yu Xiao, Dongmei Chen & Jun Wu

  2. International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China

    Huan Zhu, Yu Xiao, Dongmei Chen & Jun Wu

  3. School of Systems Science, Beijing Normal University, Beijing, 100875, China

    Huan Zhu & Dongmei Chen

Authors
  1. Huan Zhu
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  2. Yu Xiao
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  4. Jun Wu
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Correspondence to Jun Wu.

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Zhu, H., Xiao, Y., Chen, D. et al. A Robust Rating Aggregation Method based on Rater Group Trustworthiness for Collusive Disturbance. Inf Syst Front (2024). https://doi.org/10.1007/s10796-024-10489-8

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